An analog-to-digital converter (ADC) is a tool that converts analog input signals into digital signals that are represented by computers. The ADC is a bridge between the analog world and computer signal processing, and is the foundation of signal storage, transmission, and processing. Research on the ADC in the field of electronics has developed rapidly. In recent decades, electronic analog-to-digital converters (EADC) have made great progress on high sampling rate and high precision. The international commercial EADC chip can achieve the performance that the sampling rate is 30 GS/s and the effective bit is 5.5. However, due to inherent defects of the electronics technology, such as narrow processing bandwidth, large time jitter, and large transmission loss, it is of great difficulty to further break the limits of the sampling rate and sampling accuracy. New methods must be studied to realize large bandwidth analog-to-digital conversion with ultra-high sampling rate and high precision. Photonic analog-to-digital converter (PADC), as a further development of the ADC technology, can take the advantage of photonics such as ultra-high bandwidth, ultra-low time jitter, and low transmission loss, break through the limitations brought by EADC technology, and achieve analog-to-digital conversion with higher sampling rate and higher sampling accuracy. See George Valley, “Photonic analog-to-digital converter,” Optics Express, Vol. 15, No. 5, pp. 1955-1982, 2007. At the present, among various PADC schemes that have been proposed, the most concerned is the optical sampling and electrical quantization architecture. The architecture not only takes advantage of the large bandwidth and the low jitter of photonics, but also combines the mature quantitative ability of electronics. The architecture effectively breaks the electronic bottleneck. At present, the sampling rate of the PADC system in the international report has already surpasses 40 GS/s, and the effective number of bits of the reported system can reach 7˜8 bit. See G. Yang et al., “Compensation of multi-channel mismatches in high-speed high-resolution photonic analog-to-digital converter,” Opt. Express, Vol. 24, pp. 24074. However, in the architecture of the PADC, the photon sampling gate is an essential key device to the conversion process from electricity to light. The nonlinear response of the device limits the improvement on the performance of the PADC. In addition, the implementation of the multi-channel in the architecture of the PADC may also cause channel mismatch and limit the performance of the PADC.
In recent years, deep learning has received widespread attention as a technology that can effectively implement artificial intelligence. To further develop the potential of the artificial intelligence, researchers use deep learning techniques to build intelligent algorithm architectures and to implement intelligent machines that respond in ways similar to human intelligence. Most deep learning algorithms use neural network architectures. A large number of hidden layers of neural networks increase the complexity of the entire architecture, and also provide the possibility for intelligent algorithms to implement more complex functions. At present, deep learning technology has been widely used in related fields such as autonomous driving, industrial automation, human-computer interaction, image recognition, and intelligent voice. Besides, many researchers are constantly exploring in the forefront of the deep learning technology. In terms of signal reconstruction, especially noise removal, deep learning technology is widely used in the fields of image denoising, image text removal, and image demosaicing because of their adaptability to noise, accuracy of de-noising results, and the advantage of large sample applicability. See Junyuan Xie et al., “Image denoising and inpainting with deep neural network,” International Conference on Neural Information Processing Systems, pp. 341-349, 2012. Applying deep learning to the PADC system will effectively improve the performance of the PADC system, especially eliminate the limitations introduced by nonlinearity and channel mismatch, and thereby achieve analog-to-digital conversion systems with high-speed and high-precision that meet higher demands. In addition, the intelligence of deep learning provides us with an intelligent signal processing ability that may be realized in the PADC. With the ability, functions such as intelligent recognition and intelligent processing of various signals may be realized.